where \(c\)  is the target velocity. We filter demonstrations to only include expert trajectories that have at least 500 environment steps. We use the same model classes as for HalfCheetah but add in dropout to the input with \(p=0.5\)  for the sequence models. We run behavioral cloning for \(1e5\)  steps. For DAgger [1]}, we train for \(1e4\)  steps on the same set of 25 trajectories as were given to the off-policy learners and then perform 9 iterations of rollouts/aggregation/refitting, sampling 25 trajectories and training for \(1e4\)  steps. Thus, both DAgger and BC are given the same compute budget – the only difference is the data that is passed in.
